Market Context — Why This Technology, Why Now

The increasing complexity of multi-camera setups in fields like virtual production, smart surveillance, and autonomous vehicles necessitates precise synchronization and quality assurance. Manual methods are proving insufficient and costly, leading to inconsistent outputs and production delays. This technology directly addresses this by offering an automated, quantitative solution, enabling industries to scale high-quality content and enhance system reliability while navigating rising labor costs and demand for efficiency.

Key Competitive Advantages
01

Significantly Enhances Evaluation Accuracy and Uniformity: Achieves high-precision framing alignment evaluation, difficult manually, through a precise algorithm linking world and image coordinate systems. Eliminates variability in evaluation results, standardizing content quality.

02

Reduces Skilled Labor Workload by up to 80%: Automates traditional manual verification, significantly cutting framing check hours for skilled workers in video production and surveillance. Allows resource reallocation to more creative tasks.

03

Strengthens Multi-Camera and Multi-View Video Integration: Quantitatively evaluates framing alignment across different cameras. Enables seamless video integration and high-quality content generation in multi-camera video production and wide-area surveillance.

Market Opportunity
Broadcast and Video Production
$30B–$50B globally (AI est.)
The increasing demand for high-definition, multi-view content creates challenges in production quality control and efficiency. This technology reduces the burden of manual framing checks and ensures consistent content quality, contributing to production cost reduction and enhanced competitiveness.
Major broadcast networks Film and TV production studios Post-production houses Live event production companies
Surveillance and Security Systems
$1B–$2B globally (AI est.)
Smart city initiatives and facility monitoring require improved integration of multiple cameras covering wide areas and enhanced accuracy in anomaly detection. This technology could improve framing consistency across cameras, reducing oversight and contributing to advanced security system development.
Security system integrators Smart city solution providers Large-scale facility management companies Public safety technology developers
Autonomous Driving and ADAS
$50B–$100B globally (AI est.)
Integrating video information from multiple on-vehicle sensors (cameras) for accurate environmental recognition is critical for enhancing autonomous driving safety. This technology could evaluate optimal framing alignment for each camera, contributing to improved perception accuracy.
Automotive OEMs Tier 1 ADAS suppliers Sensor fusion software developers Autonomous vehicle technology companies
XR Content Production
$500M–$1B globally (AI est.)
In VR/AR and other XR content production, camera framing significantly impacts user experience in blending virtual and real spaces. This technology could support the creation of highly immersive content through real-time optimal framing evaluation.
VR/AR content studios Game development companies Immersive experience developers Metaverse platform providers
IP Defensibility — Why Competitors Can't Replicate This
What This Patent Covers

This patent protects an apparatus and program for evaluating framing alignment between a reference camera and a target camera, utilizing precise algorithms for coordinate transformation and area calculation. With 7 claims, the scope is clear and broad, having successfully navigated examination challenges to establish a robust and stable right.

Competitive White Space

White space exists in developing real-time automated framing correction systems based on this evaluation, or integrating predictive AI to anticipate optimal framing. Further IP could also be built around applying this evaluation to novel sensor fusion scenarios beyond standard camera systems.

Economic Impact
~$150K/year estimated quality control cost reduction per facility (est.)
estimated ROI · USD · AI analysis
ROI Calculation Logic

In video content production, for framing check processes, assuming an annual personnel cost of ~$100K (AI est.) for two skilled inspectors (at ~$50K/person (AI est.)), this technology could reduce inspection labor by 25%, leading to an estimated direct personnel cost reduction of ~$25K/year (AI est.). Including savings from reduced reshoots and rework due to framing errors, plus enhanced customer satisfaction from improved quality, the total economic impact could exceed ~$150K/year (AI est.).

Speed to Market
6× faster than in-house development
This technology can be integrated as a software module into existing video processing or camera control systems. The algorithms for framing region determination, conversion, and area calculation described in the patent are already established, requiring no significant hardware changes or new development. This could shorten development time by approximately 2.5 years compared to in-house development, enabling faster market entry and competitive advantage.
Competitive Positioning

X: Framing Evaluation Automation Efficiency
Y: Multi-Camera Integration Accuracy

Business Models & Applications
💻 Software License Provision
Provide the core algorithms of this technology as a software library to video production studios, surveillance system developers, and autonomous driving companies, enabling integration into their proprietary systems.
☁️ SaaS-based Framing Evaluation Service
Offer a cloud-based framing evaluation service. Licensees can upload their video data to receive detailed framing efficiency reports, enabling use with minimal initial investment.
🔌 Embedded Module Sales
Provide embedded modules implementing this technology to camera manufacturers and video equipment vendors. This allows them to integrate high-precision automatic framing evaluation features, enhancing product value and differentiation.
Adjacent Application Opportunities
🤖 ロボット制御
Optimize Industrial Robot Vision Perception
Applicable to systems where industrial robots use multiple cameras to perceive work objects. It could evaluate each camera's framing region to automatically select and adjust the most efficient and safe operational viewpoint. This could enhance robot precision and efficiency, increasing production line flexibility by an estimated 15-20%.
🏥 医療画像診断
Medical Image Diagnostic Support System
Transferable to medical imaging diagnostics like endoscopies, CT, and MRI, to automatically evaluate and suggest optimal framing for diagnostic areas. This could reduce physician workload, standardize diagnostic accuracy, and improve efficiency by up to 20%, potentially reducing patient discomfort.
🏢 スマートビルディング
Space Utilization Optimization & Anomaly Detection
In smart building management systems, it could integrate video from numerous surveillance cameras to automatically evaluate space utilization and appropriate framing during anomalies. This could enable efficient space management and rapid anomaly response, potentially improving overall building safety and convenience by 10-15%.
Integration Roadmap — Estimated 12-Month Deployment
Phase 1: Proof of Concept & Requirements Definition
Duration: 2 months
Verify the basic algorithms of this technology and its compatibility with the licensee's existing systems, defining specific implementation goals and requirements. Detail the target video data formats and camera system specifications.
Phase 2: Prototype Development & Validation
Duration: 4 months
Develop a prototype integrating the core module of this technology into existing systems based on defined requirements. Validate framing evaluation accuracy and performance under conditions similar to actual operation, and collect feedback.
Phase 3: Production System Deployment & Optimization
Duration: 6 months
Implement the system into the production environment, incorporating validation results from the prototype. Post-deployment, drive overall system optimization and functional improvements through continuous performance monitoring and operational data analysis.
Technical Feasibility
This technology primarily consists of software processing for determining, converting, and calculating the area of framing regions using world and image coordinate systems. Therefore, it can be easily integrated as a software module into existing video processing pipelines or camera control systems. By utilizing general camera parameters and ROI information, it is expected to be applicable to various camera systems held by adopting companies, without dependence on specific hardware, indicating low technical integration barriers.
Success Scenario
Upon adoption, this technology could automate up to 80% of the framing check process in video production. This could significantly reduce the burden on skilled workers, standardize content quality, and shorten time-to-release by an estimated 15%. Furthermore, integrating it into surveillance systems could enhance wide-area situational awareness in multi-camera environments, potentially improving security levels through faster anomaly detection.
Patent Record
APPLICATION NO.
特願2021-006454
REGISTRATION NO.
7614853
FILING DATE
2021/01/19
GRANT DATE
2025/01/07
EXPIRATION DATE
2041/01/19
PATENT HOLDER
日本放送協会
Examination History
2023年12月04日
出願審査請求書
2024年09月17日
拒絶理由通知書
2024年09月19日
手続補正書（自発・内容）
2024年12月03日
特許査定